Skip to main content

Linearly referenced data management, manipulation, and operations

Project description

Overview

The linref library builds on tabular and geospatial libraries pandas and geopandas to implement powerful features for linearly referenced data through EventsCollection and other object classes. Linear referencing operations powered by the numpy, shapely, and rangel open-source libraries allow for optimized implementations of common and advanced linearly referenced data management, manipulation, and analysis operations.

Some of the main features of this library include: * Event dissolves using EventsCollection.dissolve() * Merging and overlaying multiple tables of events with the EventsCollection.merge() method and the EventsMerge class API and its many linearly-weighted overlay aggregators * Linear aggregations of data such as sliding window analysis with the powerful EventsMerge.distribute() method * Resegmentation of linear data with EventsCollection.to_windows() and related methods * Creating unions of multiple EventsCollection instances with the EventsUnion object class.

Code Snippets

Create an events collection for a sample roadway events dataframe with unique route identifier represented by the ‘Route’ column and data for multiple years, represented by the ‘Year’ column. The begin and end mile points are defined by the ‘Begin’ and ‘End’ columns. >>> ec = EventsCollection(df, keys=['Route','Year'], beg='Begin', end='End')

To select events from a specific route and a specific year, indexing for all keys can be used, producing an EventsGroup. >>> eg = ec['Route 50', 2018]

To select events on all routes but only those from a specific year, indexing for only some keys can be used. >>> ec_2018 = ec[:, 2018]

To get all events which intersect with a numeric range, the intersecting() method can be used on an EventsGroup instance. >>> df_intersecting = eg.intersecting(0.5, 1.5, closed='left_mod')

The intersecting() method can also be used for point locations by ommitting the second location attribute. >>> df_intersecting = eg.intersecting(0.75, closed='both')

The linearly weighted average of one or more attributes can be obtained using the overlay_average() method. >>> df_overlay = eg.overlay_average(0.5, 1.5, cols=['Speed_Limit','Volume'])

If the events include information on the roadway speed limit and number of lanes, they can be dissolved on these attributes. During the dissolve, other attributes can be aggregated, providing a list of associated values or performing an aggregation function over these values. >>> ec_dissolved = ec.dissolve(attr=['Speed_Limit','Lanes'], aggs=['County'])

Version Notes

0.0.10 (2023-05-03)

Not a lot of updates to share, I guess that’s a good thing? * Minor updates to MLSRoute class to account for deprecation of subscripting MultiLineStrings. Most issues were addressed previously but a few were missed, most notably in the MLSRoute.bearing() method and a couple odd cases in the MLSRoute.cut() method. * Fix minor issue with EventsCollection.project_parallel() implementation related to unmatched sampling points. * Addition of EventsFrame.cast_gdf() method to cast events dataframes to geopandas geodataframes in-line. * Performance improvements * Various bug fixes, minor features

0.0.9 (2023-03-02)

First update of 2023. Been a quiet start to the year. * Add missing .any() aggregation method to EventsMergeAttribute API. Was previously available but missed during a previous update. * Update documentation * Performance improvements * Various bug fixes, minor features

0.0.8.post2 (2022-12-23)

Final update of 2022 including small feature updates and bug fixes from 0.0.8. Happy Holidays! * Add .set_df() method for in-line modification of an EventsFrame’s dataframe, inplace or not. * Addition of suffixes parameter and default setting to EventsUnion.union() method. * Performance improvements * Various bug fixes, minor features

0.0.8.post1 (2022-12-16)

  • Improve performance of .project() method when nearest=False by removing dependence on join_nearby() function and using native gpd features.

  • Add .size and .shape properties to EventsFrames and subclasses.

  • Various bug fixes, minor features

0.0.8 (2022-12-14)

  • Improve performance of essential .get_group() method, reducing superfluous initialization of empty dataframes and events collections and improving logging of initialized groups.

  • Improve performance of .union() method with updated RangeCollection.union() features and optimized iteration and aggregation of unified data. Performance times are significantly improved, especially for large datasets with many events groups.

  • Improve distribute method performance which was added in recent versions.

  • Drop duplicates in .project() method when using sjoin_nearest with newer versions of geopandas. Improved validation in .project() method, address edge case where projecting geometry column has a non-standard label (e.g., not ‘geometry’).

  • Added .sort() method to events collection. Default sorting methods remain unchanged.

  • Added warnings for missing data in target columns when initializing an EventsFrames through standard methods.

  • Remove .project_old() method from events collection due to deprecation.

  • Performance improvements

  • Various bug fixes, minor features

0.0.7 (2022-10-14)

  • Refactoring of EventsMerge system from 2D to 3D vectorized relationships for improved performance and accuracy. API and aggregation methods are largely the same.

  • Modified closed parameter use in merge relationships in accordance with rangel v0.0.6, which now performs intersections which honor the closed parameter on the left collection as well as the right collection. This provides more accurate results for events which fall on the edges of intersecting events when using left_mod or right_mod closed parameters.

  • Updates to account for rangel 0.0.6 version which is now a minimum version requirement. Added other minimum version requirements for related packages.

  • Performance improvements

  • Various bug fixes, minor features

0.0.5.post1 (2022-09-06)

  • Address deprecation of length of and iteration over multi-part geometries in shapely

  • Remove code redundancies in linref.events.collection for get_most and get_mode

0.0.5 (2022-09-01)

  • Added sumproduct and count aggregators to EventsMergeAttribute class

  • Address deprecation of length of and iteration over multi-part geometries in shapely

  • Performance improvements

  • Various bug fixes, minor features

0.0.4 (2022-06-24)

  • Minor feature additions

  • Performance improvements

  • Addition of logos in github repo

  • Various bug fixes, minor features

0.0.3 (2022-06-07)

  • Various updates for geopandas 0.10+ dependency including improved performance of project methods

  • Automatic sorting of events dataframe prior to performing dissolve

  • Performance improvements

  • Various bug fixes, minor features

0.0.2 (2022-04-11)

  • Various bug fixes, minor features

0.0.1 (2022-03-31)

  • Original experimental release.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

linref-0.0.10.post1.tar.gz (52.8 kB view details)

Uploaded Source

Built Distribution

linref-0.0.10.post1-py3-none-any.whl (54.5 kB view details)

Uploaded Python 3

File details

Details for the file linref-0.0.10.post1.tar.gz.

File metadata

  • Download URL: linref-0.0.10.post1.tar.gz
  • Upload date:
  • Size: 52.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.12

File hashes

Hashes for linref-0.0.10.post1.tar.gz
Algorithm Hash digest
SHA256 987ba66c882ee9dca100e7bc5e83e41729274bf0cbf7fae79660389aea1fe82f
MD5 3efe83be23ee427150f9bc1452723399
BLAKE2b-256 c30c4dd4a61c229af67b5018f1ee0349ad3e6052fe13dc75082efa57bcc5e72b

See more details on using hashes here.

File details

Details for the file linref-0.0.10.post1-py3-none-any.whl.

File metadata

File hashes

Hashes for linref-0.0.10.post1-py3-none-any.whl
Algorithm Hash digest
SHA256 80cd22c8b828feb110bbf452ca628cb54c28b5a205a5484a047fd0dcce45d2f6
MD5 4ee8eacd899a2bd63a17b45bcead924b
BLAKE2b-256 e1751907d089bd1a0aa8e74f590e17dbdfb0f976ecc2babc1798116155098624

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page